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Fitting for smoothing: A methodology for continuous-time target track estimation

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Title:
Fitting for smoothing: A methodology for continuous-time target track estimation.
Authors: 
Li, Tiancheng; Prieto Tejedor, Javier; Corchado Rodríguez, Juan M.
Book:
2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

Publication date: 
17 November 2016
ISSN: 
2471-917X (Online)
ISBN: 
978-1-5090-2426-1 (Print), 978-1-5090-2425-4 (Online)
DOI
 10.1109/IPIN.2016.7743582

BibTex

@conference { conference,
title = {Fitting for smoothing: A methodology for continuous-time target track estimation},
author = {Li, Tiancheng; Prieto Tejedor, Javier; Corchado Rodríguez, Juan M.},
chapter = {2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN)},
publisher = {IEEE},
isbn = {978-1-5090-2426-1 (Print), 978-1-5090-2425-4 (Online)},
year = {2016}
}

XML

<inproceedings key='conf/Li,/17 November 2016' mdate='17 November 2016'>
<author>Li</author>
<author>Tiancheng; Prieto Tejedor</author>
<author>Javier; Corchado Rodríguez</author>
<author>Juan M.</author>
<title>Fitting for smoothing: A methodology for continuous-time target track estimation</title>
<year>2016</year>
<booktitle>2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN)</booktitle>
<ee>10.1109/IPIN.2016.7743582</ee>
</inproceedings>

A preliminary framework for inferring continuous-time target trajectory (namely “track”) is given for a class of target tracking problems in which the target is subject to a rather smooth evolving process in time series, such as tracking passenger aircrafts or ships that have scheduled routes. As the core idea, the distant estimates given over time by a recursive estimator are `fitted' by using a function of continuous-time, which can be then used to infer the state for any time instants in the effective fitting period, either the past (like conventional smoothing, but curried out online) or the future (including long-term prediction). This regression analysis methodology, referred to as fitting for smoothing (F4S), also facilitates combating misdetection and outliers from which most existing tracking systems suffer. Simulations are provided to illustrate how it works and benefits in either cluttered or non-cluttered environments, with either a single target or multiple targets.

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